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Updated: Jan 13, 2026

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis
07:50

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis

Published on: December 8, 2023

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机器学习与超高性能液体染色学相结合-飞行时间质谱和气体染色学-离子移动性染色学数据融合对地理来源的歧视Lonicerae japonicae Flos

Ran Miao1,2, Minmin Zhang1,2, Xiao Wang1,2

  • 1Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.

Journal of separation science
|January 8, 2026
PubMed
概括

现在可以准确地区分Flos Lonicerae Japonicae的地理来源. 机器学习与染色体数据融合相结合,在识别真实来源方面实现了100%的准确性.

关键词:
数据融合数据融合这是一种Lonicerae japonicae的.气相色谱-离子移动性色谱机器学习是机器学习.歧视的起源 歧视的起源超高性能液态染色学 飞行时间质谱法

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